Introduction
In this study, an ensemble learning prediction model is proposed, named MulStack, which is based on random forest and deep learning for multilabel mRNA subcellular localization. This proposed model employ two levels of mRNA features, including sequence-level and residue-level, and position encoding is employed for the first time in the field of subcellular localization of mRNA. In addition, this proposed model employ One-vs-Rest strategy to train binary classifiers for each subcellular localization.
